• Journal of Biomedical Engineering Research
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• v.25 no.3
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• pp.227-233
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• 2004

In this paper, it is proposed that the four channel electrode arrangement at a frontal lobe and the noise reduction method using a JADE for the EEG biofeedback system. The proposed electrode arrangement is based on the retina-cornea dipole model. Using JADE and signals which are acquired by the proposed arrangement, four independent components are separated. To estimate a pure EEG component among four components, it is measured that a ratio of alpha wave to the whole signal and then the component that has a maximum value is considered as a pure EEG which the noise is eliminated. As a result of experiments, the proposed methods are effective in reduction of noises during acquisition of the EEG.

• Journal of Sensor Science and Technology
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• v.14 no.6
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• pp.430-437
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• 2005

In this paper, we proposed a biologically inspired light-adaptive edge detection circuit based on the human retina. A saturating resistive network was suggested for light adaptation and simulated by using HSPICE. The light adaptation mechanism of the edge detection circuit was quantitatively analyzed by using a simple model of the saturating resistive element. A light-adaptive capability of the edge detection circuit was confirmed by using the one-dimensional array of the 128 pixels with various levels of input light intensity. Experimental data of the saturating resistive element was compared with the simulated results. The entire capability of the edge detection circuit, implemented with the saturating resistive network, was investigated through the two-dimensional array of the $64{\times}64$ pixels

• Korean Journal of Ichthyology
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• v.29 no.3
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• pp.218-223
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• 2017

Using both light and scanning electron microscopies, it was investigated on the visual cells as well as the eyes of Macropodus ocellatus (Pisces, Osphronemidae). This species had a circular lens and yellowish cornea. The eyes had $3.5{\pm}0.2mm$ which is $31.1{\pm}3.0%$ in a percentage of eye diameter relative to head length. The retina ($158.2{\pm}10.6{\mu}m$) was built of several layers, including the visual cell layer which consists of three types of cells: single cons ($27.8{\pm}1.6{\mu}m$) and equal double cone ($33.9{\pm}3.7{\mu}m$), and large rods ($57.3{\pm}1.3{\mu}m$). The visual cell layer then was classified into the correct pattern. All visual cells were clearly distinguished from two parts (inner and outer segments). The elongated rod cells were extend to the bottom of the retinal pigment epithelium. In scanning electron microscopy, the outer segment links to inner segment by so-called calyceal piles. The M. ocellatus single and double cones appearance form a flower-petal arrangement, which is a regular mosaic pattern that contains quadrilateral units by four double cones surrounding a single cone.

• The Journal of Bigdata
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• v.6 no.1
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• pp.37-49
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• 2021

The number of dementia patients in Korea is estimated to be over 800,000, and the severity of dementia is becoming a social problem. However, no treatment or drug has yet been developed to cure dementia worldwide. The number of dementia patients is expected to increase further due to the rapid aging of the population. Currently, early detection of dementia and delaying the course of dementia symptoms is the best alternative. This study presented a methodology for early diagnosis of dementia by measuring and analyzing amyloid plaques. This vital protein can most clearly and early diagnose dementia in the retina through AI-based image analysis. We performed binary classification and multi-classification learning based on CNN on retina data. We also developed a deep learning algorithm that can diagnose dementia early based on pre-processed retinal data. Accuracy and recall of the deep learning model were verified, and as a result of the verification, and derived results that satisfy both recall and accuracy. In the future, we plan to continue the study based on clinical data of actual dementia patients, and the results of this study are expected to solve the dementia problem.

• Molecules and Cells
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• v.41 no.8
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• pp.771-780
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• 2018

Angiogenesis must be precisely controlled because uncontrolled angiogenesis is involved in aggravation of disease symptoms. Vascular endothelial growth factor (VEGF)/VEGF receptor 2 (VEGFR-2) signaling is a key pathway leading to angiogenic responses in vascular endothelial cells (ECs). Therefore, targeting VEGF/VEGFR-2 signaling may be effective at modulating angiogenesis to alleviate various disease symptoms. Oleanolic acid was verified as a VEGFR-2 binding chemical from anticancer herbs with similar binding affinity as a reference drug in the Protein Data Bank (PDB) entry 3CJG of model A coordination. Oleanolic acid effectively inhibited VEGF-induced VEGFR-2 activation and angiogenesis in HUVECs without cytotoxicity. We also verified that oleanolic acid inhibits in vivo angiogenesis during the development and the course of the retinopathy of prematurity (ROP) model in the mouse retina. Taken together, our results suggest a potential therapeutic benefit of oleanolic acid for inhibiting angiogenesis in proangiogenic diseases, including retinopathy.

• Journal of Korean Ophthalmic Optics Society
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• v.10 no.4
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• pp.339-345
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• 2005

In this paper, we studied the principle that correct the emmetropia to the aphakia using intraocular lens. At present, in an ophthalmic clinic, I.O.L which is using for correction of the vision clinically has presented how to prescribe more accurately by using optical method. To correct the Aphakia for the emmetropia, we considered that the basic cause of Ametropia was the refraction and the axial length. The correction principle is made equal to the Back Focal Length(BFL) and the vitreous length from lens to retina. For the confirmation of the correction principle, we used the Gullstrand number I eye model in emmetropia. For the myopia and hyperopia, we used the clinical data and replaced crystalline lens with intraocular lens.

• Korean Journal of Poultry Science
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• v.28 no.2
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• pp.143-153
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• 2001

Using bioinformatic tools for searching the massive genome databases, it is possible to Identify new genes in few minutes for initial discoveries based on evolutionary conservation, domain homology, and tissue expression patterns, followed by further verification and characterization using the bench-top works. The development of high-density two-dimensional arrays has allowed the analysis of the expression of thousands of genes simultaneously in the humans, mice, rats, yeast, and bacteria to elucidate the genes and pathways involved in physiological processes. In addition, rapid and automated protein identification is being achieved by searching protein and nucleotide sequence databases directly with data generated from mass spectrometry. Recently, analysis at the bio-chemical level such as biochemical screening and metabolic profiling (Biochemical genomics) has been introduced as an additional approach for categorical assignment of gene function. To make advantage of recent achievements in computational approaches for facilitated gene discoveries in the avian model, chicken expression sequence tags (ESTs) have been reported and deposited in the international databases. By searching EST databases, a chicken heparanase gene was identified and functionally confirmed by subsequent experiments. Using combination of sub-tractive hybridization assay and Genbank database searches, a chicken heme -binding protein family (cSOUL/HBP) was isolated in the retina and pineal gland of domestic chicken and verified by Northern blot analysis. Microarrays have identified several host genes whose expression levels are elevated following infection of chicken embryo fibroblasts (CEF) with Marek's disease virus (MDV). The ongoing process of chicken genome projects and new discoveries and breakthroughs in genomics and proteomics will no doubt reveal new and exciting information and advances in the avian research.

• Korean Journal of Optics and Photonics
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• v.17 no.2
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• pp.113-119
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• 2006

We need a good image of the retina of the human eye in order to inspect or cure it. In this work, an optical system design for a retinal camera is studied and the finite schematic eye model made by Sang Gee Kim and Sung Chan Park is used. The optical system is composed of four lens groups. The rays of the entire object field are collected on the center by the 1st group and the objective is imaged by all the other groups. The image is detected by the CCD array and displayed by a monitor The 1st lens group is employed singlet and other groups are employed triplets. Ray aberrations, spot diagrams, diffraction line spread functions and MTFs are calculated for optical performance assessment. This design may be very useful for the development of a retinal camera with high performance.

• Journal of Biomedical Engineering Research
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• v.44 no.2
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• pp.118-124
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• 2023

Many recent studies have reported that the quality of input learning data was vital to the detection of regions of interest. However, due to a lack of research on the quality of learning data on lesion detetcting using gastroscopy, we aimed to quantify the impact of quality difference in endoscopic images to lesion detection models using Image Quality Assessment (IQA) algorithms. Through IQA methods such as BRISQUE (Blind/Referenceless Image Spatial Quality Evaluation), Laplacian Score, and PSNR (Peak Signal-To-Noise) algorithm on 430 sheets of high quality data (HQD) and 430 sheets of low quality data (PQD), we showed that there were significant differences between high and low quality images in lesion detecting through BRISQUE and Laplacian scores (p<0.05). The PSNR value showed 10.62±1.76 dB on average, illustrating the lower lesion detection performance of PQD than HQD. In addition, F1-Score of HQD showed higher detection performance at 77.42±3.36% while F1-Score of PQD showed 66.82±9.07%. Through this study, we hope to contribute to future gastroscopy lesion detection assistance systems that involve IQA algorithms by emphasizing the importance of using high quality data over lower quality data.

• Journal of Korean Neurosurgical Society
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• v.66 no.1
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• pp.53-62
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• 2023

Objective : Deep learning is a machine learning approach based on artificial neural network training, and object detection algorithm using deep learning is used as the most powerful tool in image analysis. We analyzed and evaluated the diagnostic performance of a deep learning algorithm to identify skull fractures in plain radiographic images and investigated its clinical applicability. Methods : A total of 2026 plain radiographic images of the skull (fracture, 991; normal, 1035) were obtained from 741 patients. The RetinaNet architecture was used as a deep learning model. Precision, recall, and average precision were measured to evaluate the deep learning algorithm's diagnostic performance. Results : In ResNet-152, the average precision for intersection over union (IOU) 0.1, 0.3, and 0.5, were 0.7240, 0.6698, and 0.3687, respectively. When the intersection over union (IOU) and confidence threshold were 0.1, the precision was 0.7292, and the recall was 0.7650. When the IOU threshold was 0.1, and the confidence threshold was 0.6, the true and false rates were 82.9% and 17.1%, respectively. There were significant differences in the true/false and false-positive/false-negative ratios between the anterior-posterior, towne, and both lateral views (p=0.032 and p=0.003). Objects detected in false positives had vascular grooves and suture lines. In false negatives, the detection performance of the diastatic fractures, fractures crossing the suture line, and fractures around the vascular grooves and orbit was poor. Conclusion : The object detection algorithm applied with deep learning is expected to be a valuable tool in diagnosing skull fractures.